Systems and methods for automatic item classification

ABSTRACT

An item categorization service is described that automatically categorizes items of interest to a user. The user may possess an item that they wish to offer for sale using a network-based service. The user may submit item information to the item categorization service to categorize the item of interest. Upon receipt, the categorization service may assess the relevance of the item information to hierarchically organized categories maintained by the network-based service. Categories having the highest relevance may be identified as first category candidates. The deepest common ancestor of the first category candidates may be identified the first category. One or more categories, representing sub-categories of the first category, may be identified as and subjected to relevance assessment. Those sub-categories having the highest relevance may be identified as second category candidates. The deepest common ancestor of the second category candidates may be identified as a second category for the item of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/645,420, filed Dec. 22, 2009, now U.S. Pat. No. 8,805,838, which isincorporated by reference in its entirety. This application is alsorelated to U.S. application Ser. No. 12/645,405, filed Dec. 22, 2009,now U.S. Pat. No. 8,510,307, which is also incorporated by reference inits entirety.

BACKGROUND

Generally described, computing devices and communication networksfacilitate network-based commerce. For example, a user may employ his orher computing device to access a network-based retailer for the purchaseof items and services (collectively and individually referred to as“items”) such as music, books, and electronics, just to name a few.

To take advantage of this growing marketplace, sellers may elect to sellitems through network-based retailers. For example, a book-seller mayelect to sell books through a network-based retailer which offers booksfor sale. Accordingly, the seller may record some information regardingitems to be offered for sale in an electronic form, such as a writtendescription. This recorded item information may be provided to thenetwork-based retailer to enable the item to be offered for sale by thenetwork-based retailer.

Such a system may be problematic, however. A network-based retailer mayhave many possible categories into which an item may be categorized. Asa result, it may be difficult to identify one or more appropriatecategories for categorizing a seller's item. Furthermore, even if anumber of appropriate categories can be identified for an item, it maybe difficult to select the most appropriate categorization from thesechoices.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages will becomemore readily appreciated as the same become better understood byreference to the following detailed description, when taken inconjunction with the accompanying drawings, wherein:

FIG. 1 is a block diagram depicting an illustrative operatingenvironment in which an item categorization service assigns categoriesto items of interest to a user;

FIGS. 2A and 2B are diagrams depicting illustrative hierarchies of firstand second category candidates for use in assigning respective first andsecond categories for the item of interest;

FIG. 3A is a block diagram of the operating environment of FIG. 1,illustrating submission of item information to the item categorizationservice and the selection of a first category for an item of interest;

FIG. 3B is a block diagram of the operating environment of FIG. 1,illustrating identification of a search index for use in selecting asecond category for the item of interest;

FIG. 3C is a block diagram of the operating environment of FIG. 1,illustrating identification of use of the search index to select thesecond category;

FIG. 4 is a flow diagram of an illustrative routine implemented by theitem categorization service to assign categories to an item of interest;and

FIG. 5 is a schematic illustrating classifications of relevanceclustering and document distance for use in identifying methodologiesfor categorizing items of interest.

DETAILED DESCRIPTION

Generally described, embodiments of the present disclosure relate toautomatically assigning categories to items of interest to a user (e.g.,a seller of the item, such as a manufacturer, wholesaler, or retailer)based upon associated item information. The items of interest may becategorized in accordance with item categories maintained by anetwork-based service.

In this regard, an item categorization service is described thatcategorizes the items of interest on behalf of the user. For example, auser employing a user computing device may possess an item that theywish to offer for sale through the network-based service. The item maybe anything capable of being offered for sale, for example, any good,service, or information. Accordingly, the network-based service mayinclude network-based retailers such as those which sell items forpurchase to a customer, network-based sources of entertainment and/orinformation (e.g., network-based encyclopedias, media sharing, etc),network-based social networking services which enable users to sharecontent with one another, and the like.

As will be described in more detail below, in certain embodiments, theuser may prepare an electronic description of one or more items (e.g., afile including at least a written description), including itemattributes and corresponding item attribute values. For example, if anitem is a men's athletic shoe manufactured by Brand X, the itemattributes of the shoe may include, but are not limited to, gender,size, color, and the like. Item attribute values corresponding to theitem attribute may include parameters which describe the itemattributes. For example, the item attribute of “gender” may havepossible attribute values of “male” and “female.” The item attribute ofsize may have possible attribute values of “sizes 5-13.” The itemattribute of color may have possible attribute values of “red,”“yellow,” “blue,” and the like.

In certain embodiments, the user may submit the item information to theitem categorization service for categorization using a user computingdevice. In alternative embodiments, the item information may be obtainedby the item categorization service from a source other than the usercomputing device. For example, the item information may be provided bythe network-based service.

In certain embodiments, the categories may include one or more categoryattributes and respective category attribute values that describe thecategory. These categories may be collected in a first search index,which may include the category attributes and respective categoryattribute values for categories maintained by the network-based service.For example, continuing the example above, the category of “shoes” maybe described by category attributes such as “gender,” “size,” and“color.” Possible category attribute values corresponding to thecategory attributes may include, but are not limited to, genderattribute values ranging from “men's” to “women's,” size attributevalues ranging “male sizes 3-14” and “female sizes 3½ to 11½” and colorattribute values ranging between “black,” “brown,” and “white.”

The categories may also be organized hierarchically with respect to oneanother, where hierarchically inferior categories may be sub-categoriesof hierarchically superior categories. For example, the category of“shoes” may be hierarchically superior to a sub-category such as“athletic shoes”, and hierarchically inferior to a category such as“footwear.” Furthermore, in certain embodiments, the category attributevalues of a selected category may be sub-categories of the selectedcategory. For example, a category of “shoes” may include attributes of“gender,” “size,” and “color,” any or all of which may be sub-categoriesof “shoes.”

Upon receipt of the item information, the item categorization servicemay compare the item information to categories maintained in a searchindex stored in one or more data structures (e.g., databases). Incertain embodiments, the comparison performed by the item categorizationservice may include a relevance assessment of the item information withrespect to the categories. The relevance assessment may include one ormore mathematical operations that quantify the degree to which acategory matches, or is similar to, the item information.

The relevance values determined in this manner may be employed inconjunction with an analysis of category hierarchies to determine afirst category for assignment to the item of interest. For example, aselected number of categories (e.g., the top five) which exhibit thehighest relevance to the item information for the item of interest maybe identified as first category candidates. The respective positions ofeach of the first category candidates within a hierarchy of categoriesmay be further examined to determine a deepest common ancestor shared bya majority of the first category candidates (e.g., at least three out offive). This common ancestor may be selected to be the first category.

In an embodiment, the assigned first category may be further refinedthrough the use of one or more rules. Briefly, the rules may specify oneor more category attributes for the first category whose correspondingattribute values may refine the categorization of the item of interestbeyond the first category. For example, assuming that the first categoryis “shoes,” and the category attribute of “gender” is identified by therules, category attribute values such as “men's” or “women's” mayfurther refine the first category of “shoes” (e.g., “men's shoes” or“women's shoes”).

In order to accomplish this refinement of the first category, the valuesof the category attributes identified by the rules are determined. Inone aspect, one or more categories may be identified, each of whichinclude the category attributes of the first category identified by therules. This collection of categories may be referred to as restrictedcategories, to distinguish them from other collections of categorieswhich are not constrained by limitations on the content of the categoryattributes by which they are described.

In another aspect, the restricted categories may be employed in a secondrelevance comparison operation with the item information for the item ofinterest. Based upon the second relevance assessment, a selected numberof the restricted categories (e.g., the top five) may be identified assecond category candidates. The hierarchical relationships of the secondcategory candidates may be examined and the deepest common ancestorshared by a majority of the second category candidates may be assignedas the second category. The process of second category assignment may berepeated for as many rules and respective category attributes associatedwith the first category as are identified.

To facilitate performing the relevance comparisons, in certainembodiments, the unrestricted and restricted categories may bemaintained in separate search indices and/or different data structureswithin the same search index. For example, a first search index maystore unrestricted categories for use in assigning a first category tothe item of interest. In another example, a second search index mayinclude restricted categories, for use in assigning one or more secondcategories to the item of interest. Second search indices may each beprepared for selected category attributes, as necessary, (e.g., a searchindex for each of “gender,” “size,” “color,” and the like). The firstand second search indices may include any item categories maintained bythe network-based service.

In a further embodiment, a confidence level may be determined for theitem categorization discussed above on the basis of relevance clusteringand category distances. Relevance clustering refers to a measure of thespacing between a selected number of relevance values assessed for anitem of interest and unrestricted categories, as discussed above (e.g.,a selected number of categories having the highest relevance values). Asdiscussed in greater detail below, relevance values of pairwisecombinations of selected categories may be aggregated and the aggregatedrelevance classified as either tightly clustered, where the relevancevalues for the item of interest are relatively close to one another, orloosely clustered, where the relevance values for the item of interestare relatively far from one another.

Category distances may refer to measurement of the similarity betweenselected categories. For example, the selected categories may be thecategories having the highest relevance values discussed above. Asdiscussed below, the respective category distances between pairwisecombinations of selected categories may be aggregated and the aggregatedcategory distance classified as either close or far. Close categorydistances indicate that the categories are relatively similar to oneanother, while far category distances indicate that the categories arerelatively dissimilar to one another.

By combining the relevance clustering and category distances, differentconfidence levels in the item categorization may be established. Forexample, assuming relevance clustering results may be classified intotight and loose clustering and category distances may be classified intofar and close distances, four different combinations of relevanceclustering and category distances may be identified, each having adifferent confidence level: tight relevance clustering and closecategory distance, tight relevance clustering and far category distance,loose relevance clustering and close category distance, and looserelevance clustering and far category distance.

In alternative embodiments, other routines for categorizing items ofinterest using relevance assessments are also envisioned by embodimentsof the present disclosure. In an embodiment, the categorization approachdiscussed above may be modified. The manner in which the first andsecond category candidates are selected remains the same, however,categories are assigned on the basis of a statistical analysis of thelikelihood of category pairings, rather than by selection of a commonancestor. For example, the statistical likelihood that pairs of each ofthe respective first and second category candidates are combinedtogether may be determined. The categories having the highest likelihoodof being in combination may be assigned to the item of interest. It maybe understood that this mechanism is not limited to pairs of categoriesbut may be extended to any combination of first and second categorycandidates. For example, statistical likelihoods may be examined for afirst category candidate and multiple second category candidates (e.g.,“shoes” and “men's” and “brown”) and the group of categories having thehighest likelihood of being combined may be assigned to the item ofinterest.

In another embodiment, relevance assessments of unrestricted categoriesmay be employed with specific category attributes to identify a firstcategory and one or more second categories. For example, an unrestrictedcategory may be described by an attribute “first category” havingpossible attribute values of “yes” or “no.” The unrestricted categoryhaving the highest relevance value and further having an attribute valueof “yes” for the attribute “first category” may be assigned as the firstcategory. At least a portion of unrestricted categories having higherrelevance values than the first category and an attribute value of “no”for the attribute “first category” may be assigned as second categories.

With reference to FIG. 1, an illustrative operating system 100 is shown,including an item categorization service 102 that categorizes items ofinterest. For example, as discussed in greater detail below, the itemcategorization service 102 may assign categories to the item of interestbased upon associated item information submitted by the user computingdevice 104. The category assignments may be determined based upon therelevance of the item information to item categories (e.g., unrestrictedand restricted categories) maintained by a network-based service 106. Incertain embodiments, the network-based service 106 may be anetwork-based retail service implemented via a website that offers oneor more items for sale. Items categorized by the item categorizationservice 102 may be stored by the network-based service 106 in a datastore 120 and presented within one or more categories of items offeredfor sale by the network-based service 106.

It may be recognized that many of the components described below areoptional and that embodiments of the system 100 may or may not combinecomponents. Components need not be distinct or discrete. Components maybe reorganized in the system 100. The system 100 may be represented in asingle physical server containing all of the subsystems described belowor, alternatively, the system may be split into multiple physicalservers. For example, in certain embodiments, the item categorizationservice 102 may be housed within the network-based service 106. Inalternative embodiments, the item categorization service 102 may includea stand-alone service. In additional embodiments, the itemcategorization service 102 may be housed within one or more usercomputing devices 104.

The item categorization service 102 and network-based service 106 mayeach be embodied in a plurality of components, each executing aninstance of the respective item categorization service 102 ornetwork-based service 106. A server or other computing componentimplementing the item categorization service 102 or network-basedservice 106 may include a network interface, memory, processing unit,and computer readable medium drive, all of which may communicate whicheach other may way of a communication bus. The network interface mayprovide connectivity over a network 108 and/or other networks orcomputer systems. The processing unit may communicate to and from memorycontaining program instructions that the processing unit executes inorder to operate the item categorization service 102 or network-basedservice 106. The memory generally includes RAM, ROM, and/or otherpersistent and auxiliary memory.

In one embodiment, the user computing device 104 may communicate withthe categorization service 102 and network-based service 106 via acommunication network 108, such as the Internet or a communication link.Those skilled in the art will appreciate that the network 108 may be anywired network, wireless network or combination thereof. In addition, thenetwork 108 may be a personal area network, local area network, widearea network, cable network, satellite network, cellular telephonenetwork, or combination thereof. Protocols and components forcommunicating via the Internet or any of the other aforementioned typesof communication networks are well known to those skilled in the art ofcomputer communications and thus, need not be described in more detailherein.

The user computing device 104 may include any computing device, such asa laptop or tablet computer, personal computer, personal digitalassistant (PDA), hybrid PDA/mobile phone, mobile phone, electronic bookreader, set-top box, camera, digital media player, and the like. Theuser computing device 104 may also be any of the aforementioned devicescapable of receiving or obtaining data regarding an item of interestfrom another source, such as a digital camera, a remote control, anothercomputing device, a file, etc.

In embodiments discussed below, the item categorization service 102 ofFIG. 1 may assign categories to an item of interest in response toreceipt of item information regarding the item of interest from the usercomputing device 104. It may be understood, however, that the itemcategorization service 102 may also be employed to assign categories inresponse to receipt of item information from the network-based service106. For example, the network-based service 106 may store records ofprior item information submissions. It may be desirable to provide thisitem information to the item categorization service 102 in order toverify and/or update the categories assigned to the items described bythe previously submitted item information.

With further reference to FIG. 1, the categorization service 102 mayinclude one or more components for use in categorizing items of interestdescribed by submitted item information. In one embodiment, thecategorization service 102 may include a service interface component110. The service interface component 110 may enable the categorizationservice 102 to receive item information (e.g., from the user computingdevice 104 or other computing resource in communication with the itemcategorization service 102 via the network 108) and transmit iteminformation which has been updated to include one or more categories forthe item of interest (e.g., to the network based service 106).

In further embodiments, the categorization service 102 may furtherinclude a rule component 112. The rule component 112 may be employed toidentify attributes for a selected category that may be employed infurther categorization operations.

The item categorization service 102 may additionally include an analysiscomponent 114 for performing a variety of analyses with respect to theitem information and categories in order to assign categories to theitem of interest. In embodiment, the analysis component may determinethe relevance between items of interest and selected item categories, aswell as similarities between categories. The analysis component 114 mayfurther be employed to examine the hierarchies of selected categoriesand determine their deepest common ancestors. The analysis component 114may also assign categories to items of interest based upon thestatistical likelihood of pairings of categories. The analysis componentmay further assign categories to items of interest based upon relevanceclustering and category distances. It may be understood that this listis not exhaustive and other analytical operations may be performed bythe analysis component 114, as necessary, without limit.

The item categorization service 102 may further be in communication withone or more search indices maintained in one or more data stores,represented by data store 116. The data store 116 may includeinformation representing one or more categories of items. Thesecategories of items may reflect categories maintained by thenetwork-based service 106, enabling items of interest categorized by theitem categorization system 102 to be easily added to the categories ofthe network-based service 106. This information may include, but is notlimited to, attributes and respective attribute values associated withthe categories, referred to as category attributes and categoryattribute values for clarity. It may be understood, however, that itemattributes and item attribute values may be the same as categoryattributes and category attribute values, the distinction of “item” and“category” being used to distinguish the attribute or attribute valuebeing discussed.

The categories maintained by the data store 116 may also be grouped in avariety of ways. In one aspect, the categories may include any of thecategories maintained by the network-based service 106. This grouping ofcategories may be referred to herein as unrestricted categories, as nolimitations are placed upon the categories. In another aspect,categories may be grouped according to possession of a categoryattribute value corresponding to a selected category attribute. Thisgrouping of categories may be referred to herein as restrictedcategories. The categories may be further organized hierarchically withrespect to one another.

The service interface component 110 may enable a user of the usercomputing device 104 to submit item information to the itemcategorization service 102. For example, an application executing on theuser computing device 104 may generate one or more user interfaces thatenable communication with the service interface component 110 forsubmission of item information regarding an item of interest. The iteminformation may further include one or more item attributes and itemattribute values for the item of interest. The item information may besubmitted in formats including, but not limited to, flat files. Inalternative embodiments, the service interface component 110 may enablethe user to directly input item information without storing the iteminformation in a file.

The analysis component 114 may be employed to assign categories to theitem of interest based upon selected analysis of the categories and thesubmitted item information. In one embodiment, the analysis component114 may be employed to perform a relevance assessment of the receiveditem information and one or more of the unrestricted categories storedby the data store 116. Relevance represents a measurement of topicalrelevance or aboutness. That is to say, how well a topic of a result(e.g., the one or more categories) matches an information need (e.g.,the item information submitted for the item of interest). The relevanceassessment the item information to a selected category may be made usingany mathematical framework understood by those of skill in the art todetermine the similarity between the item information (e.g., itemattributes and/or item attribute values) and the category (e.g.,category attributes and/or category attribute values).

In certain embodiments, the relevance of a category to the iteminformation may be determined on the basis of vectors representing theitem information for the item of interest and the category. For clarity,vectors representing item information for the item of interest may bereferred to as item vectors, while vectors representing the categoryattributes and/or category attribute values of a selected category maybe referred to as category vectors. The vectors include a mathematicalrepresentation of at least a portion of the text information includedwithin the received item information and selected categories (e.g.,respective item attributes and item values of the item information andrespective category attributes and category attribute values of theselected category).

In certain embodiments, a vector space model may be employed to generatethe item and category vectors. For example, each dimension of an item orcategory vector may correspond to a separate term of their respectiveitem information or category. Thus, if a term occurs in the iteminformation, the value of that term in the item vector may be non-zeroand similarly for the category vector. In certain embodiments, the valueof a term in an item vector may include the frequency with which theterm occurs in the item information. In further embodiments, the valuesof each of the terms of the item vector may be further weighted, usingweighting schemes understood in the art. Examples of such weightingschemes may include, but are not limited to, term frequency-inversedocument frequency modeling (tf-idf).

The analysis component 114 may compare the item and category vectors toassess the relevance between the item information and the selectedcategory. In an embodiment, the vector comparison may include thedeviation of the angles between the item vector and the category vectorunder consideration. In another embodiment, the vector comparisons mayinclude the cosine of the angle between the item vector and the categoryvector under consideration. The resulting relevance may range betweenzero to one, with zero indicating no relevance between the item ofinterest and the selected category and one indicating a high relevancebetween the item of interest and the selected category. It may beunderstood that this description of vector comparisons is not exhaustiveand that other methods of calculating relevance known in the art may beemployed within the scope of the disclosed embodiments.

The analysis component 114 may also choose one or more unrestrictedcategories as candidates for a first category, according to theirassessed relevance. In one embodiment, a selected number of theunrestricted categories having the highest relevance values may bechosen as candidates for the first category. For example, the fiveunrestricted categories having the highest relevance may be chosen asthe first category candidates.

The analysis component 114 may further analyze the respectivehierarchies of the first category candidates in order to determine thefirst category to be assigned to the item of interest. The deepestcommon ancestor of the first candidate categories (e.g., at least threeout of five) may be selected to be the first category. In certainembodiments, the deepest common ancestor may also include one of thefirst candidates themselves.

FIG. 2A illustrates one embodiment of this first category selectionprocess. Assume that category candidates of “shoes,” “men's shoes,”“women's shoes,” “men's boots,” and “men's athletic shoes” aredetermined to be the five unrestricted categories having the highestrelevance to the item of interest, illustrated in the category hierarchyof FIG. 2A as underlined. It may be observed from FIG. 2A that “shoes”is a common ancestor to a majority of these category candidates (e.g.,“shoes,” “men's shoes,” “women's shoes,” and “men's athletic shoes.”Therefore, “shoes” may be assigned as the first category to the item ofinterest.

The rule component 112 may be employed to identify rules that specifyone or more category attributes for the first category for use indetermining one or more second categories that refine the categorizationprovided by the first category. For example, the rule component 112 maycommunicate with the data store 116 to retrieve one or more such rules.The rule component 112 may further transmit this information to theanalysis component 114 for use in assigning the second category.

Upon receipt of the category attributes identified by the rules, theanalysis component 114 may further obtain restricted categories thatinclude the specified category attributes. For example, continuing theexample above, if the first category is “shoes” and the rules componentspecifies the category attribute of “gender,” then the appropriaterestricted categories may include the “gender” attribute and anattribute value for “gender” (e.g., male or female).

A second relevance assessment of the item of interest and the restrictedcategories may be performed, as discussed above. Second categorycandidates may be chosen from the restricted categories having thehighest relevance (e.g., the highest five). Furthermore, the commonancestor possessed by a majority of the second category candidates(e.g., three out of five) may be selected to be the second category.This process may be repeated for each of the category attributesspecified by the rule component 112.

Continuing the example begun above, assuming a category attributespecified by the rules component is “gender,” the hierarchy of thesecond category candidates may be examined to determine the appropriatecategory attribute value (e.g., “men's” or “women's”). Further assumethat second candidates of “women's shoes,” “men's casual shoes,” “men'sathletic shoes,” “men's golf shoes,” and “men's dress shoes” aredetermined from the second relevance assessment, illustrated asunderlined in FIG. 2B. From FIG. 2B, it may be observed that “men'sshoes” is the most common ancestor of the second category candidates.Therefore, “men's shoes” or “men's” may be assigned to the item ofinterest as the second category.

Although described as components of the item categorization service 102,the service interface component 110, the rule component 112, and/or theanalysis component 114 may be discrete components from the itemcategorization service 102. Accordingly, the categorization service 102may include one or more interface components for communication with theservice interface component 110, the rule component 112, and/or theanalysis components 114 via the network 108.

In further embodiments, the categorization service 102 may be housedwithin one or more user computing devices 104 and operate as discussedabove. For example, categories, both unrestricted and restricted, may beretrieved from the data store 116. In alternative embodiments,categories may be stored by the data store 116 and pushed to the usercomputing device 104 when updated. Beneficially, by storing thecategories locally, and pushing updates to the user computing devices104, the user computing devices 104 may employ the item categorizationservice 102 residing locally to categorize items of interest to the userwithout the need for communication with the network 108.

FIG. 3A is a block diagram of the operating environment of FIG. 1,illustrating the submission of a request to categorize an item ofinterest to the item categorization service 102. In certain embodiments,the request may be submitted from the user computing device 104 to thecategorization service 102 via one or more other computing devices(e.g., network based service 106). In alternative embodiments, therequest may be submitted directly to the categorization service 102. Thecategorization request may include, but is not limited to, iteminformation regarding the item of interest.

In one embodiment, the item information may be obtained by the usercomputing device 104 prior to submission of the request. For example,the item information may be obtained as one or more files from anothercomputing device through the network 108. In another example, the iteminformation may be obtained by generating one or more files containingthe item information on the user computing device 104. In one example, arequest to categorize the item of interest may be submitted byidentifying one or more files containing item information and tosubmitting the identified files containing the item information to thecategorization service 102.

Upon receipt of the request, the item categorization service 102 mayemploy the item information to assign a first category to the item ofinterest. For example, the item categorization service 102 may retrievea first search index including unrestricted categories from the datastore 116. The item categorization service 102 may further determinefirst category candidates from a relevance assessment of the iteminformation and the unrestricted categories and analyze the hierarchicalrelationships of the first category candidates to identify their deepestcommon ancestor. This deepest common ancestor may be assigned as thefirst category.

FIG. 3B is a block diagram of the operating environment of FIG. 1,illustrating submission of the assigned first category to the data store116 by the item categorization service 102. In response to receiving thesubmitted first category, the data store 116 may return one or morerules identifying category attributes for the first category that may beused in refinement of the assigned first category. The itemcategorization service 102 may further identify one or more secondsearch indices that maintain restricted categories which possess thecategory attributes identified by the rules.

FIG. 3C is a block diagram of the operating environment of FIG. 1,illustrating use of a second search index containing restrictedcategories for selection of a second category corresponding to the firstassigned category. The item categorization service 102 may determinesecond category candidates from a second relevance assessment betweenthe item information and the restricted categories. The hierarchicalrelationships of the second category candidates may also be analyzed toidentify their deepest common ancestor, which may be assigned as asecond category. Subsequently, the item information, including theassigned first category and one or more assigned second categories, maybe returned to the network-based service 106 for storage in data store120.

FIG. 4 is a flow diagram of an illustrative routine 400 implemented bythe item categorization service 102 to assign categories to an item ofinterest. The routine 400 begins in block 402, where the itemcategorization service 102 receives a request to categorize an item ofinterest, including item attributes and item attribute values.

In block 404, the item categorization service 102 may identify firstcategory candidates from a first search index. As discussed above, thefirst search index may include unrestricted categories and may beobtained from the data store 116 or other data storage device. Theidentification may include performing a relevance assessment of at leasta portion of the unrestricted categories included within the firstsearch index. A selected number of the unrestricted categoriesexhibiting the highest relevance values may be identified as the firstcategory candidates.

In block 406, a first category may be selected using the first categorycandidates and assigned to the item of interest. In certain embodiments,the categories may be hierarchically organized. As such, the respectivecategory hierarchies of the first category candidates may be examinedand the deepest common ancestor of the first category candidates,assigned as the first category to the item of interest.

In block 410, an identification may be made as to one or more categoryattributes of the first category which may be employed to further refinethe categorization provided by the first category. In one aspect, theidentification may be made by obtaining one or more rules from the datastore 116 or other data storage device.

In decision block 411, a determination may be made as to whether anyrules for the first assigned category are found in the data store 116.If one or more rules are found, the routine 400 moves to block 412,where a second category may be identified for the item of interest. Ifone or more rules are not found, the routine 400 moves to block 424,where the annotated item information, including the assigned firstcategory, may be transmitted to the network-based service 106.

In block 412, a second search index may be obtained that is based uponthe category attributes specified by the obtained rules. In certainembodiments, the second search index may include categories that areconstrained to include the category attribute identified by the rules,as well as the respective category attribute values. The second searchindex may be obtained from the data store 116 or other data storagedevice, as necessary.

In block 414, the item categorization service 102 may identify one ormore second category candidates from the second search index. Asdiscussed above, the second search index may include restrictedcategories and second category candidates may be identified byperforming a relevance assessment between the item information and atleast a portion of the restricted categories maintained by the secondsearch index. A selected number of the restricted categories exhibitingthe highest relevance to the item of interest may be identified as thesecond category candidates.

In block 416, a second category may be selected using the secondcategory candidates and assigned to the item of interest. In certainembodiments, the categories within the second search index may behierarchically organized. The respective category hierarchies of thesecond category candidates may be examined and the deepest commonancestor of the second category candidates assigned as a second categoryto the item of interest.

In decision block 420, a determination may be made as to whetheradditional category attributes remain to be evaluated from theidentified classification rule. If there are additional categoryattributes having values that remain to be evaluated, the routine 400returns to block 412, where a new search index corresponding to aselected one of the remaining category attributes may be obtained andblocks 412-416 are repeated using the new search index to determine newsecond category candidates and another second category.

If there are no additional category attributes that remain to beevaluated, the routine 400 moves to block 422. In block 422, adetermination may be made as to whether another rule specifyingadditional category attributes remains. In one embodiment, such a rulemay be obtained based upon the first assigned category. In otherembodiments, the rule may be obtained based upon an assigned secondcategory. If additional rules remain, the routine 400 may move to block410, where one of the additional rules may be identified and the routine400 continues through blocks 410-416 as discussed above to assignanother second category to the item of interest.

If there are no additional rules which remain to be identified, theroutine 400 may move to block 424, where updated item information,including a first assigned category and one or more second assignedcategories, may be transmitted to the network-based service 106. At thenetwork based service 106, the item information may be stored by thedata store 120, enabling the item of interest to be present appropriatecategory maintained by the network-based service 106, enabling the itemof interest to be easily found and purchased. In alternativeembodiments, the updated item information may be stored by the itemcategorization service 102 for transmission to the network-based service106 or other computing device at a later date.

In a further embodiment, a confidence level in the categorizationdiscussed above may be assigned on the basis of relevance clustering andcategory distances. Relevance between the item information for the itemof interest and selected unrestricted categories may be assessed asdiscussed above. Relevance clustering may characterize the amount bywhich the relevance values of selected categories are separated from oneanother. The selected categories may include, but are not limited to, aselected number of unrestricted categories having the highest assessedrelevance. For example, in an embodiment, the assessed relevance valuesmay be aggregated and compared to a threshold in order to classify therelevance clustering of the item categorization. In one embodiment, theaggregated relevance values may be represented by the maximum pairwisedifference between assessed relevance values. In another embodiment, theaggregated relevance values may be represented by an average pairwisedifference between assessed relevance values. In a further embodiment,the aggregated relevance values may be represented by a sum of thepairwise differences between respective assessed relevance values. Inadditional embodiments, relevance values represented by a sum mayinclude sums that are weighted based upon their respective relevancevalues.

The clustering of relevance values may be further classified accordingto their separation distance as compared with a threshold value. Incertain embodiments, item categorizations having aggregated relevancevalues greater than the threshold value may be classified as looselyclustered, while item categorizations having aggregated relevance valuesless than the selected value may be classified as tightly clustered. Itmay be understood that alternative mechanisms for determining theaggregated relevance values and classifying the nature of the clusteringmay be employed without departing from the spirit of the disclosedembodiments.

Category distances may refer to the similarity of categories withrespect to one another. Specifically, the categories of interest in thiscontext may include the selected number of unrestricted categorieshaving the highest assessed relevance to the item of interest. Incertain embodiments, the category distance may be determined using avector analysis, as discussed above with respect to the relevanceassessment. In this case, though, the vector analysis is performed onthe basis of vectors representing the categories under examination. Avector space model may be employed to generate the respective categoryvectors and the analysis component 114 may assess the distance betweenrespective category vectors.

The assessed category distances may be aggregated and compared to acategory threshold in order to classify the category distance of theitem categorization. In one embodiment, the aggregated category distancemay be represented by the maximum pairwise separation between assessedcategory distances. In another embodiment, the aggregated categorydistance may be represented by the average pairwise difference betweenassessed category distances. In a further embodiment, the aggregatedcategory distance may be represented by the sum of the pairwisedifferences between respective assessed relevance values. In additionalembodiments, category represented by a sum may include sums that areweighted based upon their respective relevance values. Aggregatedcategory distances greater than the threshold value may be classified asfar category distance, while aggregated category distances less than thethreshold may be classified as close category distance. It may beunderstood that alternative mechanisms for determining the categorydistance and classifying the nature of the category distance may beemployed without departing from the scope of the present disclosure.

Having classified the relevance clustering and category distances, theseclassifications may be combined to establish a confidence level for theitem categorization discussed above. For example, based uponclassifications for the relevance clustering of tight and loose andclassifications for the category distances as close and far,combinations of tight clustering and close distance, tight clusteringand far distance, loose clustering and close distance, and looseclustering and far distance may be established, as illustrated in FIG.5. Based upon the respective classification of the item of interest inthis relevance clustering-document distance space, different routinesmay be employed for determining categories for the item of interest.

As illustrated in FIG. 5, a classification of tight relevance clusteringand close category distance may provide a high confidence level of thatunrestricted categories possessing a high relevance to the item ofinterest are an accurate categorization of the item of interest.Notably, relevance values do not provide an absolute measure ofrelevance but rather a relative measure of relevance. For example,absent additional information, a close clustering of relevance valuesmay indicate that analyzed unrestricted categories possess near equallypoor relevance or may indicate that the unrestricted categories possessnear equally good relevance. The result that the category distances areclose, however, indicates that the unrestricted categories are similarto one another. Therefore, the close category distance, in combinationwith the tight relevance clustering, provides high confidence that thehighest relevance unrestricted categories are good representations ofthe item of interest.

As further indicated in FIG. 5, loose relevance clustering and closecategory distance, as well as loose clustering and far categorydistance, may provide a medium-high to medium level of confidence inassessed relevance of the unrestricted categories. In general, aclassification of loose relevance clustering, whether the categorydistance is close or far, will yield a level of confidence in therelevance assessment which is lower than that obtained in the case oftight clustering and close document distance.

Loose relevance clustering may indicate that some of the unrestrictedcategories may have poor relevance with the item of interest, whileother unrestricted categories may have good relevance with the item ofinterest. The finding of close category distance, though, may indicatethat the categories are relatively similar to each other. Therefore, inabsolute terms, at least some of the uncategorized candidates may berelatively good category candidates for the item of interest, with thoseunrestricted categories having relative high relevance likely to be thebest category candidates.

Loose relevance clustering in combination with far document distance mayindicate that some of the unrestricted categories may have good or poorrelevance with the item of interest and that the categories arerelatively dissimilar to one another. Therefore, in absolute terms,those categories having relatively high relevance are likely to bebetter category candidates for the item of interest than those with lowrelevance.

As also indicated in FIG. 5, tight relevance clustering and far documentdistance indicates low confidence in the relevance assessment. Asdiscussed above, the relevance assessment provides a relative, notabsolute measure of relevance of the unrestricted categories to the iteminformation for the item of interest. Tight clustering may be equallyindicative of that the unrestricted categories are relatively relevant,in an absolute sense, as relatively irrelevant. The addition of the fardocument distance, however, indicates that the unrestricted categoriesare relatively dissimilar to one another. In view of this observation,it may be concluded that the tightly clustered categories may have arelatively poor relevance with the item of interest.

In further embodiments, the confidence level may be employed to assignalternative routines for item categorization. For example, in the caseof tight relevance clustering and close category distance, the followingcategorization routine may be followed. In one embodiment, one or moreattribute values associated with the unrestricted category having thehighest relevance value may be assigned as categories. This rulereflects the observation that, the close category distance, incombination with the tight relevance clustering, provides highconfidence that the unrestricted category having the highest assessedrelevance is a likely to be a good representation of the item ofinterest. For example, assuming that the category having the highestassessed relevance is “clothes” and that this category further possessesat least attribute values of “silk” and “pajamas,” at least one of“silk” and “pajamas” may be assigned to the item of interest ascategories.

As discussed above, in the case of loose relevance clustering and closecategory distance or loose relevance clustering and far categorydistance, those results having relative high relevance are likely to bebetter category candidates for the item of interest. Thus, embodimentsof the routine for assigning first and second categories for the item ofinterest may be employed as discussed above with respect to FIGS. 2A-2C.

Under circumstances of tight relevance clustering and far categorydistance, the item of interest may be alternatively categorized asfollows. A selected number of the unrestricted categories having thehighest relevance values (e.g., the top five) may be identified as firstcategory candidates. The deepest common ancestor of each of the firstcategory candidates may then identified by examining the categoryhierarchy of each of the respective first category candidates andassigned as the first category. In the case that no common ancestor isidentified for all of the first category candidates, the itemcategorization service 102 may fail to return a first category.Additionally, in certain embodiments, no identification of a secondcategory may be made under these conditions, reflecting the observationthat, whatever category is assigned to the item of interest from thefirst category candidates, it is likely to be a poor representation ofthe item of interest.

In alternative embodiments, other procedures for categorizing items ofinterest using relevance assessments are also envisioned by the presentdisclosure. In one embodiment, relevance assessments of unrestrictedcategories maintained by the network-based service 106 may be employedwith specific category attributes to identify the first category and oneor more second categories. For example, each of the unrestrictedcategories may be described by an attribute “first category” that haspossible attribute values including “yes” and “no,” which representwhether or not the unrestricted category may be considered as a firstcategory. It may be understood that other names for such an attributemay be ascribed without limit.

As discussed above, the item categorization service may assess therelevance of the item information to the unrestricted categories toorder the unrestricted categories in terms of descending relevancevalues. The unrestricted category having the highest relevance andfurther described by the category attribute value of “yes” to thecategory attribute “first category” may be assigned as the firstcategory of the item of interest. One or more of the unrestrictedcategories having higher relevance values than the first category may beassigned as second categories of the item of interest. In certainembodiments, all of the unrestricted categories having higher relevancevalues than the first category may be assigned as second categories ofthe item of interest

For example, assume that for an item of interest “Men's Running Shoes,Black, Brand X,” the unrestricted categories of highest relevance,listed from highest to lowest relevance, are: “men's athletic shoes,”“athletic shoes,” “shoes,” “women's shoes,” and “men's boots.” Furtherassume that of these categories, “shoes” is the unrestricted categoryhaving the highest relevance value and further having an attribute valueof “yes” for the “first category” attribute. Therefore, the firstcategory assigned to the item of interest may be “shoes.” Either or bothof the categories of “men's athletic shoes,” and “athletic shoes,” whicheach possess relevance values higher than “shoes,” may be assigned assecond categories for the item of interest.

In another embodiment, a relevance assessment may be combined withstatistical analysis of the likelihood of category pairings in order todetermine a first and one or more second categories for the item ofinterest. In order to assign categories in this manner, first and secondcategory candidates may be identified, as discussed above, usingrelevance assessments of unrestricted and restricted categories and oneor more rules. Notably, however, first and second categories are notassigned in some embodiments to the item of interest based upon thedeepest common ancestor of the first and second category candidates.

Instead, to establish which of the first and second category candidatesis to be assigned to the item of interest, a statistical analysis may beperformed to assess the likelihood that a first category candidate maybe combined with a second category candidate. For those pairings havinga statistical likelihood less than a threshold value, the pairing offirst and second category candidates may not be assigned as categoriesto the item of interest. However, for those pairings having astatistical likelihood greater than the threshold value, the pairing offirst and second category candidates may be assigned to the item ofinterest.

The statistical likelihood of pairings of first and second categorycandidates may be generated using statistical frameworks for likelihoodcalculations, as known in the art. In certain embodiments, thelikelihood pairing first and second category candidates may be generatedprior to receiving a request to categorize the item of interest and maybe stored for later retrieval in the data store 116. For example, theanalysis component 114, or other computing device in communication withthe item categorization service 102, may generate and store likelihoodsof pairings of first and second category candidates. The storedlikelihood values may also be periodically updated, as necessary. Inother embodiments, the likelihood of pairing the first and secondcategory candidates may be determined by the analysis component 114, orother computing device in communication with the item categorizationservice 102, during the process of assigning the first and secondcategory candidates to the item of interest.

For example, assume that for an item of interest, “silk pajamas” thefirst category candidates, listed from highest to lowest relevance, are:“clothes,” “lingerie,” “shoes,” “pajamas,” and “pants,” with a deepestcommon ancestor of “clothes.” Further assume that, for the category“clothes,” a rule is obtained which identifies “material” as categoryattribute for “clothes” that may be employed for refining “clothes.”From this rule, second category candidates of “flannel,” “silk,”“denim,” “satin,” and “corduroy” may be identified. Assuming that thelikelihood threshold is 0.65 and the likelihood of pairing the category“pajamas” with “silk” is 0.95, “silk” and “pajamas” may each be assignedas categories of the item of interest, as their likelihood of pairinglies above the selected threshold. Further assuming that the likelihoodof “lingerie” and “corduroy” is 0.10, this pairing of first and secondcategories may not be assigned to the item of interest, as theirlikelihood of pairing is less than the threshold.

It may be understood that more than one pairing of first and secondcategories may be assigned in this manner. Continuing the example above,assume that “clothes” and “silk” possess a likelihood of 0.90. While thelikelihood of this pairing is not as high as “pajamas” and “silk,” itstill exceeds the threshold of 0.65 and, therefore, these categories mayalso be assigned to the item of interest.

All of the processes described herein may be embodied in, and fullyautomated via, software code modules executed by one or more generalpurpose computers or processors. The code modules may be stored in anytype of computer-readable medium or other computer storage device. Someor all the methods may alternatively be embodied in specialized computerhardware. In addition, the components referred to herein may beimplemented in hardware, software, firmware or a combination thereof.

Conditional language such as, among others, “can,” “could,” “might” or“may,” unless specifically stated otherwise, are otherwise understoodwithin the context as used in general to convey that certain embodimentsinclude, while other embodiments do not include, certain features,elements and/or steps. Thus, such conditional language is not generallyintended to imply that features, elements and/or steps are in any wayrequired for one or more embodiments or that one or more embodimentsnecessarily include logic for deciding, with or without user input orprompting, whether these features, elements and/or steps are included orare to be performed in any particular embodiment.

Any process descriptions, elements or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or elements in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown, or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and protected by the following claims.

What is claimed is:
 1. A system for categorizing items, the systemcomprising: a network-based service configured to: receive a request tocategorize an item of interest, the request including item informationregarding the item of interest; and transmit the request to an itemcategorization service; a data store configured to store categories foritems, wherein at least a portion of the categories are hierarchicallyorganized with respect to one another; and the item categorizationservice, wherein the item categorization service is in communicationwith the data store and the network-based service, and wherein the itemcategorization service is adapted to: assign a first category to theitem of interest by: identifying a plurality of first categorycandidates based at least in part upon a relevance value associated withthe item information and one or more of the categories for items storedby the data store, wherein at least a portion of the first categorycandidates are hierarchically organized; determining which of the firstcategory candidates has the most first category candidate attributes ofthe first category candidates; and selecting the first categorycandidate that has the most first category candidate attributes as thefirst category of the item of interest; and assign a second category tothe item of interest by: determining one or more rules associated withthe first category of the item of interest; identifying a plurality ofsecond category candidates using the rules; and selecting a secondcategory of the item of interest from the plurality of second categorycandidates.
 2. The system of claim 1, wherein the item categorizationservice is further configured to transmit updated item information,including the first and second categories of the item of interest, tothe network-based service.
 3. The system of claim 1, wherein itemcategorization service is further adapted to assign the second categoryto the item of interest based at least in part on a confidence levelassociated with the item information and the plurality of first categorycandidates.
 4. The system of claim 1, wherein the first and secondcategories each comprise one or more category attributes and associatedcategory attribute values.
 5. The system of claim 4, wherein each of thesecond category candidates comprises a selected category attribute andassociated category attribute value of the first category.
 6. A systemfor categorizing items, the system comprising: a data store adapted tostore a plurality of hierarchically organized categories for items, eachof the hierarchically organized categories including at least onecategory attribute and corresponding category attribute value; and anitem categorization service in communication with the data store, theitem categorization service adapted to: receive item informationassociated with an item of interest, the item information comprising atleast one item attribute and corresponding item attribute value for theitem of interest; assign a first category for the item of interest fromthe hierarchically organized categories stored by the data store usingthe item information; determine one or more rules associated with theassigned first category; identify one or more second category candidatesusing the one or more rules and a relevance value associated with theitem information and the at least one second category candidate;determine which of the second category candidates has the most secondcategory candidate attributes of the second category candidates; andselect the second category candidate that has the most second categorycandidate attributes as the second category of the item of interest. 7.The system of claim 6, wherein the item categorization service isfurther adapted to assign the first category for the item of interestbased at least in part on relevance values associated with at least aportion of the hierarchically organized categories and the iteminformation.
 8. The system of claim 7, wherein the item categorizationservice is further adapted to assign the first category for the item ofinterest by identifying the hierarchically organized categories havingthe highest relevance values as first category candidates.
 9. The systemof claim 8, wherein the item categorization service is further adaptedto assign the first category for the item of interest by selecting adeepest common ancestor of the first category candidates, wherein thedeepest common ancestor comprises the first category candidate that is acommon ancestor to more of the first category candidates than any otherfirst category candidates.
 10. The system of claim 6, wherein the itemclassification service is further adapted to identify a categoryattribute of the assigned first category and select the first categoryfrom a plurality of first category candidates, and wherein at least oneof the first categories is not associated with the identified categoryattribute of the assigned first category.
 11. The system of claim 6,wherein the item classification service is further adapted to identify acategory attribute and corresponding category attribute value associatedwith the second category candidate and determine relevance valuesassociated with the at least one category attribute and correspondingcategory attribute value of the at least one second category candidateand the received at least one item attribute and corresponding itemattribute value.
 12. The system of claim 6, wherein the itemcategorization service is further adapted to update the item informationfor the item of interest with the assigned first category and theassigned second category and to transmit the updated item information tothe data store.
 13. The system of claim 6, wherein the itemclassification service is further adapted to identify a categoryattribute of the assigned first category using the one or more rules.14. The system of claim 6, wherein the item classification service isfurther adapted to assign the second category of the item of interest byselecting a deepest common ancestor of hierarchically organized selectedsecond category candidates, wherein the deepest common ancestorcomprises the selected second category candidate in the hierarchicallyorganized selected second category candidates that is common to more ofthe hierarchically organized selected second category candidates thanany other selected second category candidate.
 15. A computer-implementedmethod for categorizing items, the method comprising: under control ofone or more computer systems configured with specific instructions:receiving item information associated with an item of interest, the iteminformation comprising one or more item attributes and correspondingitem attribute values; assigning a primary category describing the itemof interest by selecting the primary category using a first searchindex, wherein the first search index comprises one or morehierarchically organized first categories having first attributes andcorresponding first attribute values; determining one or more rulesassociated with the assigned primary category; obtaining a second searchindex comprising one or more hierarchically organized second categoriesusing the one or more rules; identifying one or more secondary categorycandidates from the second categories based at least in part uponrelevance values associated with the item information and each of theone or more hierarchically organized second categories; determiningwhich of the second category candidates has the most second categorycandidate attributes of the second category candidates; and selectingthe second category candidate that has the most second categorycandidate attributes as a secondary category describing the item ofinterest.
 16. The computer-implemented method of claim 15, whereinassigning the primary category describing the item of interest from thefirst search index further comprises determining relevance valuesassociated with at least one of the first attributes and associatedfirst attribute values of respective first categories and the receiveditem attributes and item attribute values.
 17. The computer-implementedmethod of claim 16, wherein assigning the primary category describingthe item of interest from the first search index further comprisesidentifying a selected number of the first categories based on theirrelevance values as first category candidates.
 18. Thecomputer-implemented method of claim 17, wherein assigning the primarycategory describing the item of interest using the first search indexfurther comprises selecting a deepest common ancestor of the firstcategory candidates as the primary category, wherein the deepest commonancestor comprises the first category candidate that is common to moreof the first category candidates than all other first categorycandidates.
 19. The computer-implemented method of claim 15, wherein atleast one of the first categories included in the first search indexdoes not comprise a category attribute or the primary category.
 20. Thecomputer-implemented method of claim 15, further comprising determiningrelevance values associated with at least one category attribute andrespective category attribute values of respective second categories andthe received item attributes and item attribute values.
 21. Thecomputer-implemented method of claim 15, further comprising updating theitem information with the assigned primary category and the assignedsecondary category and transmitting the updated item information forstorage.
 22. A computer-readable medium having instructions storedthereon, wherein the instructions, when executed by a computingapparatus, cause the computing apparatus to: receive item informationassociated with an item of interest, the item information comprising oneor more item attributes and corresponding item attribute values; assigna first category for the item of interest from hierarchically organizedfirst categories stored by a data store; determine one or more rulesassociated with the assigned first category; identify one or morehierarchically organized second category candidates using the one ormore rules and relevance values associated with the item information andeach of the second category candidates; determine which of the secondcategory candidates has the most second category candidate attributes ofthe second category candidates; and select the second category candidatethat has the most second category candidate attributes as the secondcategory of the item of interest.
 23. The computer-readable medium ofclaim 22, wherein the instructions that cause the computing apparatus toassign the first category for the item of interest from the firstcategories comprise instructions that cause the computing apparatus todetermine relevance values between at least one category attribute andcategory attribute value of respective first categories and the receiveditem attributes and corresponding item attribute values.
 24. Thecomputer-readable medium of claim 23, wherein the instructions thatcause the computing apparatus to assign the first category for the itemof interest from the first categories further comprise instructions thatcause the computing apparatus to identifying first category candidatesfrom first categories using the determined relevance values.
 25. Thecomputer-readable medium of claim 24, wherein the instructions thatcause the computing apparatus to assign the first category for the itemof interest from the first categories further comprise instructions thatcause the computing apparatus to select a deepest common ancestor of thefirst category candidates, wherein the deepest common ancestor comprisesthe first category candidate that is common to more first categorycandidates than any other first category candidate.
 26. Thecomputer-readable medium of claim 22, wherein at least one of the firstcategories does not comprise a category attribute of the assigned firstcategory.
 27. The computer-readable medium of claim 22, wherein theinstructions further cause the computing apparatus to determine therelevance values between the item information and each of the secondcategory candidates by determining relevance values between at least onesecond category candidate attribute and associated second categorycandidate attribute value and the received item attributes and itemattribute values.
 28. The computer-readable medium of claim 22, furthercomprising instructions that cause the computing apparatus to update theitem information with the assigned first category and the assignedsecond category and to transmit the updated item information to a datastore.